Data Engineer, Machine Learning

Data Engineer, Machine Learning

San Francisco, CA · In Person · Full-Time

Applying to this role will also allow us to consider you for other research opportunities at Vizcom. We believe the best roles are shaped around exceptional people, not just job descriptions.

About Vizcom

Vizcom is where design teams at companies like Nike, GM, New Balance, and Hasbro bring ideas from sketch to product. Designers use Vizcom to sketch, render, explore color and materials, work in 3D, and prepare concepts for production.

The render itself was never the point. The point is the physical thing that comes after it. We call this pencil to product .

Vizcom is a Series B company with more than $52M raised.

More than 700,000 designers have worked in Vizcom, and every session leaves a trail: candidates selected, outputs promoted into designs, regions masked and renamed, and entire directions kept or discarded.

That trail is one of the most valuable things we create outside of the product itself. Today, though, it's more archaeology than asset. Only a fraction of what happens in a session reaches training-grade quality, while increasingly sophisticated post-training methods depend on exactly this kind of high-quality, domain-specific data.

Your job will be to help turn that trail into a machine.

A design session isn't a simple sequence — it's a branching tree. Designers fork, backtrack, iterate, and abandon entire directions on their way to the thing they ultimately keep. The judgment lives in the shape of that process, and today we capture only pieces of it.

The Role

As an ML Data Engineer , you'll build and improve the systems that turn professional design work into training-grade preference data and training results back into a better product.

You'll work alongside the researchers consuming what you build, within the product systems where these signals originate, and across the data infrastructure where they ultimately land. Your closest users are the researchers sitting beside you, and you'll see quickly when a dataset you've built allows them to ask a question they couldn't ask before.

This is not a support role, and it isn't traditional offline ETL.

The pipelines you work on will run through a live product used every day by professional design teams, including enterprise customers with rigorous expectations around privacy and data protection. Capturing better signals without compromising user trust, contractual obligations, or product performance is a core part of the work.

If you want to train models without building the systems that feed them, this probably isn't the role for you. If you believe the next advances in ML will increasingly be won through better data, it might be.

We think about a dataset as a product: it has users, versions, provenance, and a quality bar. A training result should be reproducible from a dataset fingerprint months later, and "Where did this example come from?" should always have an answer.

Here, helping build that standard is the job.

What You'll Own

• The capture surface: Partner with Product and Engineering to improve what the product records and help design and ship instrumentation in production systems.

• The data pipeline: Build and maintain the path from canvas to warehouse to training set, ensuring data is clean, versioned, reliable, and reproducible.

• Dataset contracts and lineage: Implement systems that make examples traceable to their origin and training datasets reproducible over time.

• Privacy and data boundaries: Build systems that reflect what can be captured and used under different enterprise agreements, with consent, isolation, and appropriate safeguards designed in from the beginning.

• Research-ready datasets: Create appropriately governed and sanitized datasets that allow researchers to experiment safely and effectively.

• Collection instruments: When historical signals aren't enough, help build mechanisms for gathering explicit feedback that designers actually want to use.

• Honest representations of ambiguity: Design data systems that preserve context. Unpicked doesn't necessarily mean disliked, abandoned doesn't necessarily mean rejected, and our data should reflect the difference.

• Data quality: Build checks, monitoring, and tooling that make it easier to identify gaps, inconsistencies, and unexpected changes before they affect research or training.

This is a charter, not a week-one checklist. We don't expect one person to tackle everything at once. You'll work with the broader team to prioritize the areas where you can have the most impact and expand your scope over time.

What Your First 90 Days Could Look Like

Days 1–30: Map

Understand the event surface, warehouse, existing datasets, research workflows, and current data boundaries. Identify what exists, what's missing, and which research questions our data can't yet answer.

Get comfortable with the existing pipeline and begin contributing improvements to data quality, reliability, or observability.

Days 30–60: Ship

Take one new signal end to end: instrumented in the product, landed reliably in the warehouse, versioned appropriately, and available to researchers.

Document the implementation and validate that the resulting dataset behaves as expected.

Days 60–90: Build the standard

Own a meaningful improvement to how we version, validate, trace, or govern ML data and contribute to the standards the team uses for dataset fingerprints, lineage, quality, and data boundaries.

Identify follow-up opportunities based on what you've learned and begin taking ownership of a broader area of the ML data stack.

What We're Looking For

• Experience building data infrastructure, ML data systems, or production data pipelines used by other teams.

• Strong software engineering skills and experience building reliable production systems.

• Experience designing data models and pipelines with reproducibility, observability, and lineage in mind.

• Comfort working with large or complex datasets and debugging issues across multiple parts of a data system.

• An experimental mindset and comfort working closely with researchers to turn ambiguous questions into measurable datasets.

• Good judgment around data quality, including an understanding of when the absence of a signal is meaningfully different from a negative signal.

• Comfort operating in an environment where the underlying systems and standards are still evolving.

• A collaborative approach to engineering, including documenting decisions, incorporating feedback, and working across Product, Engineering, and Research.

Nice to Have

• You've trained models yourself and understand what ML training pipelines actually need from their data.

• You've worked with preference data, labeling systems, human-feedback pipelines, or evaluation operations.

• You've built data infrastructure under meaningful privacy, security, or contractual constraints.

• You've worked with large-scale event or behavioral datasets.

• You have experience with data systems supporting generative AI or multimodal models.

Above all, we're looking for someone who can look at the exhaust of a complex product and see evidence .

What You'll Get

• A unique dataset: The recorded decisions and workflows of more than 700,000 designers, with new signals generated every day.

• Researchers as your users: You'll work directly alongside the people using the datasets you build, creating an unusually tight feedback loop between data engineering and research.

• Meaningful ownership: You'll own substantive pieces of Vizcom's ML data infrastructure and have room to expand your scope as you build context and expertise.

• A rare problem space: High-quality professional preference data is difficult to create. You'll have the opportunity to build systems around a dataset and domain that few ML teams have access to.

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